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title: Card Image Classifier Comparison | |
colorFrom: yellow | |
colorTo: blue | |
sdk: gradio | |
app_file: app.py | |
pinned: false | |
license: mit | |
# Card Image Classifier Comparison | |
[Kaggle page](https://www.kaggle.com/datasets/gpiosenka/cards-image-datasetclassification/data) | |
Data: 53 classes 7624 train, 265 test, 265 validation images 224 X 224 X 3. | |
The train, test and validation directories are partitioned into 53 sub directories, one for each of the 53 types of cards. The dataset also includes a csv file which can be used to load the datasets. | |
## Plan: | |
* Fine-tune a pretrained image classification model | |
* Compare a few small efficient ones | |
* Use old PyTorch utility functions | |
* If enough time, redo in Lightning and write utility functions for it | |
* Extract outputs from the feature extraction layers and try completing the task with gradient boosting | |
* Compare results | |
## Comparison of best models of each type | |
The following shows the training and performance benefits of gradient boosting over NN classification layers. | |
Compare these models in [their huggingface space](https://t-flet-kaggle-cards.hf.space) (or clone the repo and python app.py). | |
| **Model** | **Retrained Portion** | **Epochs** | **Time** | **test_loss** | **test_F1** | | |
| ----- | ----- | ----- | ----- | ----- | ----- | | |
| **RexNet 1.0** | Full Retrain | val_loss early stop at 5 (equalling 2)| 16:32 | 0.0996 | 1 | | |
| **RexNet 1.5 features -> LightGBM** | No retraining; feature extraction -> GB | 100 bagging gbdt iterations (OpenCL, not CUDA) | 3:37 | | 0.5433 | | |
| **RexNet 1.5** | Classification | 10 | 8:00 | 1.5839 | 0.4884 | | |